Software Methodology for Estimating the Efficiency of the Hardware Composition of Deep Packet Inspection System Using the Modernized Hooke ‒ Jeeves Method
https://doi.org/10.31854/1813-324X-2021-7-1-132-140
Abstract
Deep packet inspection systems on communication networks are used to identify the application generating a specific traffic flow. The issues related to modeling and design of deep packet inspection systems remain poorly understood. In this paper, a software technique for evaluating the effectiveness of the hardware composition of the servers of the deep packet inspection system is presented, using a mathematical model of such a system and software search methods. The description of the program search by the maximum element method and the Hook-Jeeves method is given. A modernization of the Hook-Jeeves method for a monotonically decreasing function is proposed. Comparison of the methods by the number of search steps is performed.
About the Author
V. Fitsov
The Bonch-Bruevich Saint-Petersburg State University of Telecommunications
Russian Federation
Russian Federation
References
1. Senchenko Yu.L. Some Aspects of High-Speed Traffic Processing. Tekhnologii i sredstva sviazi. 2013;1(94):52−53. (in Russ.)
2. Trammell B., Boschi E., Procissi G., Callegari C., Dorfinger P., Schatzmann D. Identifying Skype Traffic in a Large-Scale Flow Data Repository. Proceedings of the 3rd International Workshop on Traffic Monitoring and Analysis, TMA, 27 April 2011, Vienna, Austria. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; 2011. vol.6613. p.72−85. DOI:10.1007/978-3-64220305-3_7
3. Sommer R., Feldmann A. NetFlow: Information loss or win? Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment, IMW '02, November, 2002, Marseille, France. New York: Association for Computing Machinery; 2002. p.173‒174. DOI: 10.1145/637201.637226
4. Park J., Yoon S., Kim M. Software Architecture for a Lightweight Payload Signature-Based Traffic Classification System. Proceedings of the 3rd International Workshop on Traffic Monitoring and Analysis, TMA, 27 April 2011, Vienna, Austria. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; 2011. vol.6613. p.136−149. DOI:10.1007/978-3-642-20305-3_12
5. Deart V., Mankov V., Krasnova I. Agglomerative Clustering of Network Traffic Based on Various Approaches to Determining the Distance Matrix. Proceedings of the 28th Conference of Open Innovations Association, FRUCT’28, 27‒29 January 2021, Moscow, Russia. IEEE; 2021. vol.1. p.81−88. DOI:10.23919/FRUCT50888.2021.9347616
6. Dainotti A., Pescape A., Sansone C. Early Classification of Network Traffic through Multi-classification. Proceedings of the 3rd International Workshop on Traffic Monitoring and Analysis, TMA, 27 April 2011, Vienna, Austria. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; 2011. vol.6613. p.122−135. DOI:10.1007/978-3-642-20305-3_11
7. Sheluhin O., Kazhemskiy M. Influence Of Fractal Dimension Statistical Charachteristics On Quality Of Network Attacks Binary Classification. Proceedings of the 28th Conference of Open Innovations Association, FRUCT’28, 27‒29 January 2021, Moscow, Russia. IEEE; 2021. vol.1. p.407−413. DOI:10.23919/FRUCT50888.2021.9347600
8. Cascarano N., Ciminiera L., Risso F. Optimizing Deep Packet Inspection for High-Speed Traffic Analysis. Journal of Network and Systems Management. 2011;19:7−31. DOI:10.1007/s10922-010-9181-x
9. Niang B. Bandwidth management ‒ A deep packet inspection mathematical model. Proceedings of the 6th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT, St. Petersburg, Russia, 6-8 October 2014. IEEE; 2014. p.169−175. DOI:10.1109/ICUMT.2014.7002098
10. Goldstein B., Fitsov V. Dual Mathematical Model for Calculating of Deep Packet Inspection. Proceedings of the 28th Conference of Open Innovations Association, FRUCT’28, 27‒29 January 2021, Moscow, Russia. IEEE; 2021. vol.1. p.127−133. DOI:10.23919/FRUCT50888.2021.9347547
11. Fitsov V. Methods for Constructing Network Architectures of Dpi Systems. Vestnik Svyazi. 2020;12:32−37. (in Russ.)
12. Fitsov V. Employment Software Code for Optimization the Number of Dpi-Servers by Maximal Element Method. Proceedings of the VIIIth International Conference on Infotelecommunications in Science and Education, 28 February ‒ 1 March 2018, St. Petersburg, Russia. St. Petersburg: The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Publ.; 2018. p.650−656. (in Russ.)
13. Yakimovich, S. Controlling Traffic and Services in Broadband Networks Using DPI Solutions. Vestnik Svyazi. 2010;12: 27−29. (in Russ.)
14. Rec. ITU-T Y.2771 Framework for deep packet inspection. ITU; 2014.
15. Norros I. A storage model with self-similar input. Queueing Systems. 1994;16:387−396. DOI:10.1007/ BF01158964
16. Ovcharov L. Applied Problems of Queuing Theory. Moscow: Mashinostroenie Publ; 1969. 324 p. (in Russ.)
17. Hooke R., Jeeves T.A. «Direct Search» Solution of Numerical and Statistical Problems. Journal of the ACM. 1961;8(2): 212−229. DOI:10.1145/321062.321069
18. Sulimov V.D., Shkapov P.M., Nosachev S.K. Hooke–Jeeves Method-used Local Search in a Hybrid Global Optimization Algorithm. Science and Education. 2014;6:107−123. DOI:10.7463/0614.0716155 (in Russ.)
Review
For citations:
Fitsov V. Software Methodology for Estimating the Efficiency of the Hardware Composition of Deep Packet Inspection System Using the Modernized Hooke ‒ Jeeves Method. Proceedings of Telecommunication Universities. 2021;7(1):132-140. (In Russ.) https://doi.org/10.31854/1813-324X-2021-7-1-132-140
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